Commonsense Reasoning.
Prior work in Natural Language Processing has studied the problem of imbuing commonsense knowledge in AI systems, from social common-sense knowledge [1]}, [2]}, [3]}, [4]}, [5]}, [6]} to understand the likely intents, goals, and social dynamics of people, abductive commonsense reasoning [7]}, next event prediction [8]}, [9]}, to temporal common sense knowledge about temporal order, duration, and frequency of events [10]}, [11]}, [12]}, [13]}. Most similar to our work is the study of physical commonsense knowledge [14]} about object affordances, interaction, and properties (such as flexibility, curvature, porousness). However, these benchmarks are static in nature (as a dataset of textual or visual prompts). Our task, on the other hand, is instantiated in an embodied interactive environment and more realistic – the environment is partially observed, and the agent has to explore unseen regions, discover misplaced objects and use common-sense reasoning to infer compatibility between objects and receptacles.
